The present disclosure is related to sensor clustering in networks. More specifically, the present disclosure is related to clustering of wireless cameras in a network for tracking objects that move through the respective fields of vision of the cameras.
In event driven sensor clustering, it is necessary to select a subset of a group of sensors which provide data about the event source. Various sensors in the network must communicate to share information about the event to establish useful information regarding the event. While some sensors may provide useful information, not all sensors in the group provide useful information all of the time. For example, a sensor may not be positioned to detect the event.
In order to minimize the processing resources necessary to track the event, it is known to establish a cost function to select the appropriate subset of sensors so that useful information is maximized while the cost of resources to process the information is minimized. In the case of omnidirectional sensors, such as microphones for example, clustering of subsets of sensors relies on the known physical relationship between the sensors to evaluate event-generating target information for the cost function. The position and spacing between the sensors defines a relationship which is considered in the cost function algorithm.
In the case of directionally limited sensors, such as cameras, for example, position and distance based criteria for the sensors may be of limited value in evaluating the cost function. The cost of carrying out a given task depends on the amount of energy required by a set of sensors to process the information and the amount of communication traffic generated during this process. In directional sensors, the cost is associated with the relative orientations of sensors that are able to collaboratively carry out the same task. Proximal sensors may sense segments of space that are disjointed or spaced apart from one another.